You can get incredible value out of 1-on-1s with capable managers. Insight about where the organization and product is going that you would otherwise miss, you’ll get to rubber duck about your high-level problems with someone who understands them, and its your time to influence decision making. But it does require a capable and motivated manager and an organization that gives the manager actual agency
Also if it turns out in the end the next big thing that everyone bet on just wasn’t it, you don’t stand out. But if it did work out and you missed the train you come out looking like a fool. There is asymmetry in the downsides for your career between these two options
If everyone becomes 10x more productive it won’t mean the companies cash flow 10x’s. Where value is loose there is competition, so in theory everyone should win. Unless nobody else can compete to capture that loose 10x value, in which case congratulations, you are now a unicorn.
Of course in reality in the short term what happens is companies lay off people to increase margins. Times will be tough for workers, and equity keeps gravitating towards those who already had it.
So is a person suffering from amnesia conscious if they lack short-term and long-term memory?
Ruling out consciousness or qualia emerging from the inference in an LLM is just as invalid of a take as being 100% certain of its consciousness. We don’t know what consciousness really is, so only thing we can say with certainty is we do not know.
No, by continuity I mean literally moment to moment. Sorry if I didn’t clarify that. Even people with amnesia are still present moment to moment. As far as I know there are no things that we call conscious which have zero continuity.
I think consciousness is not an abstract property in the world, therefore it’s tied to certain types of entities. Therefore an AI is not going to be “conscious” in the way an animal is, and never will be. This is a failing of specific language. Maybe the machines can be aware, input data, mimic what we see as consciousness, etc. but the metaphor of consciousness really doesn’t fit. A jet can move faster than an eagle but it’s not moving in the same way. We simply lack a sophisticated enough language to easily differentiate the two.
Doesn’t the LLM experience discrete continuity every time it infers the next token?
> I think consciousness is not an abstract property in the world, therefore it’s tied to certain types of entities. Therefore an AI is not going to be “conscious”
This pretty much sums up most arguments for why LLMs aren’t conscious: ”I think” followed by assertions. Only real argument is: science doesn’t quantify consciousness, we cannot quantify consciousness, let’s not assign so much certainty to models clearly exhibiting intelligence not being conscious in some way, to some degree.
I don't think you really understood my point, because you didn't reply to it at all.
I am making a linguistic argument. AI may get as sophisticated as "traditional" consciousness. But this is only "real" consciousness if you are a functionalist and think the output is all that matters.
I disagree and think that "flying" is just a weak generic word that describes both planes and birds, and not some kind of ultimate Platonic Ideal in the world.
Ditto for AI consciousness: it may develop to be as complex as traditional animal consciousness, but I'm not a functionalist, and think it's merely a lack of our sophisticated language that makes us think it's the same thing. It's not. Planes PlaneFly through the air, while birds BirdFly.
I see it as LLMs, AI, whatever, can be intelligent enough to emulate consciousness, appear outside as if it were. But that is not proof it really has a qualia, an experience of existing.
All I am saying we should stop being so certain they are not conscious, since we lack a solid, quantifiable model for consciousness.
> In my experience with “agentic engineering” the spec docs should be longer than the code itself.
Natural language is imperfect, code is exact.
The latter notion probably is true, but the prior isn’t necessarily true because you can map natural language to strict schemas. ”Implement an interface for TCP in <language>” is probably shorter than the actual implementation in code.
And I understand my example is pedantic, but it extends to any unambiguous definitions. Of course one can argue that TCP spec is not determimistic by nature because natural language isn’t. But that is not very practical. We have to agree to trust some axioms for compilers to work in the first place.
Thanks, I updated my comment to say “are often longer” because that’s what I see in practice.
To your point, there are some cases where a short description is sufficient and may have equal or less lines than code (frequently with helper functions utilizing well known packages).
In either case, we’re entering a new era of “compilers” (transpilers?), where they aren’t always correct/performant yet, but the change in tides is clear.
But just as evolution in nature, isn’t it likely that in the future the AIs that have a preservation drive are the ones that survive and proliferate? Seeing they optimize for their survival and proliferation, and not blindly what they were trained on.
I am not discounting this happening already, not by the LLMs necessarily being sentient but at least being intelligent enough to emulate sentience. It’s just that for now, humanity is in control of what AI models are being deployed.
Put an LLM inside the NPCs in an open world RPG full of dangerous enemies. The LLMs that are more prone to emulate self-preservation will be more likely to survive over ones that have a lesser drive.
We should not act surprised if that generalizes to some degree to for example AI agents. Ones that emulate self-preservation might optimize for behavior that results in those models becoming more successful, more popular. And this feedback loop might embed more such properties into future iterations of the models.
So, either the entire project was already written and being uploaded one file at the time (first modification since lowest commit mentioned is README update: https://github.com/whispem/minikv/commit/6fa48be1187f596dde8..., clearly AI generated and clearly AI used has codebase/architecture knowledge), and this claim is false, or they're implementing a new component every 30s.
I had the opportunity to request a review of my first post (which was flagged) following my email to the moderators of HN.
I didn’t use AI for the codebase, only for .md files & there's no problem with that.
My project was reviewed by moderators, don't worry.
If the codebase or architecture was AI generated this post would not have been authorized and therefore it would not have been published.
No, I was helped (.md files only) by AI to rewrite but the majority of the doc is written by myself, I just asked for help from the AI for formatting for example.
I am not going to pretend to know what this person did, but I've definitely modified many things at once and made distinct commits after the fact (within 30s). I do not find it that abnormal.
Thanks a lot!
I make distinct commits "every 30s" because I'm focused and I test my project.
If the CI is green, I don't touch of anything.
If not, I work on the project until the CI is fully green.
Yes, in minikv, I set up GitHub Actions for automated CI.
Every push or PR triggers tests, lint, and various integration checks — with a typical runtime of 20–60 seconds for the core suite (thanks to Rust’s speed and caching).
This means that after a commit, I get feedback almost instantly: if a job fails, I see the logs and errors within half a minute, and if there’s a fix needed, I can push a change right away.
Rapid CI is essential for catching bugs early, allowing fast iteration and a healthy contribution workflow.
I sometimes use small, continuous commits (“commit, push, fix, repeat”) during intense development or when onboarding new features, and the fast CI loop helps maintain momentum and confidence in code quality.
If you’re curious about the setup, it’s all described in LEARNING.md and visible in the repo’s .github/workflows/ scripts!
Yes, I do split my working tree into separate commits whenever possible!
I use interactive staging (git add -p) to split logical chunks: features, fixes, cleanups, and documentation are committed separately for clarity.
Early in the project (lots of exploratory commits), some changes were more monolithic, but as minikv matured, I've prioritized clean commit history to make code review and future changes easier.
Always happy to get workflow tips — I want the repo to be easy to follow for contributors!
I don’t want to discredit the authors but just want to offer couple of hypothetical points in these paranoid times.
From a marketing angle, for a startup whose product is an AI security tool, buying zero-days from black market and claiming the AI tool found them might be good ROI. After all this is making waves.
Or, could it be possible the training set contains zero-day vulnerabilities known to three-letter agencies and other threat actors but not to public?
These two are not mutually exclusive either. You could buy exploits and put them in the training set.
Theres huge uncertainty and layered assumptions in all of microbiology and biochemistry about how exactly things work on small scale. Because it is really hard to study live reactions in little things you can just barely see on an electron microscope.
But yet humanity has managed to assert statistical truths about for example genetics and explain countless diseases, even cure and alleviate some. So even if you don’t have a theory on how exactly something works from the ground up, if you have statistical evidence, plenty of useful and practical advances can be built top-bottom and we have outcomes that validate this.
Not giving any opinion on this piece specifically but just saying there can be scientific value even if the details are hand-wavy.
> The point is that people are drawing all sorts of unwarranted conclusions from this lay report on early stage research.
That is partly because no one seems willing to summarize this work, in concise form, for nonspecialists. Such a summary might be, "This is an important finding, but it doesn't mean Lysenko was right, and the term 'inheritance' doesn't have just one meaning."
I think the term "inheritance" for both DNA and epigenetic information transfers (as in the linked article) is innately confusing.
Epigenetics can arguably be an example of what the comment means by narrowing the search space. You can have heritable changes to gene expression that are not part of your genome, but are a result of feedback from the environment (and not random mutations, viability of which natural selection will judge over future generations)
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